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Topic: Neuroevolution

Related Topics

Neural networks

NeuroEvolution of Augmented Topologies

Computational learning theory

Pattern recognition

Time series prediction

Artificial intelligence

Classification

Neural networks

	[No title] (Site not responding. Last check: 2007-10-19)
		Neuroevolution is particularly well suited to video games because (1) it works well in high-dimensional spaces; (2) diverse populations can be maintained; (3) individual networks behave consistently; (4) adaptation takes place in real time; and (5) memory can be implemented through recurrency (Gomez et al., 2006; Stanley et al., 2005).
		Although neuroevolution methods are still being developed, the technology can already be used to make current games more challenging and interesting and to implement entirely new genres of games.
		Neuroevolution may also make it possible to build effective training games, that is, games that adapt as the trainee’s performance improves.
	www.nae.edu /nae/bridgecom.nsf/BridgePrintView/MKEZ-6WHQWF?OpenDocument (4267 words)

	Neuroevolution - Encyclopedia, History, Geography and Biography
		Neuroevolution, or neuro-evolution, is the use of genetic algorithms to train artificial neural networks.
		It is useful for applications such as games and robotic motor control, where it is easy to measure a network's performance at a task but difficult or impossible to create a syllabus of correct input-output pairs for use with a supervised learning algorithm.
		ANNEvolve is an Open Source AI Research Project (Has downloadable source code in C and Python for a variety of interesting problems.
	www.arikah.net /encyclopedia/Neuroevolution (237 words)

	Neuroevolution based artificial bandwidth expansion of telephone band speech patent invention
		Neuroevolution based artificial bandwidth expansion of telephone band speech patent invention
		Neuroevolution based artificial bandwidth expansion of telephone band speech
		The fitness evaluation module may be configured to compare the artificially expanded wideband speech signal to a corresponding speech sample in the learning sample management module to determine if the artificially expanded wideband speech signal is similar to the original wideband sample of speech.
	www.freshpatents.com /Neuroevolution-based-artificial-bandwidth-expansion-of-telephone-band-speech-dt20051201ptan20050267739.php (1649 words)

	[No title]
		However, neuroevolution have been seen, the output weights W (the output layer in fig- is rarely used for supervised learning tasks such as time se- ure 1) are computed using linear regression from to D. The ries prediction because it has difficulty fine-tuning solution column vectors in (i.e.
		The error in the predic- ods, to address the disadvantages of each, extending ideas tion or the residual error is then used as the fitness measure proposed for feedforward networks of radial basis functions to be minimized by evolution.
		The input gate "protects" a neuron from its input: only vide a fitness statistic that is used to produce better neurons when the gate is open, can inputs affect the internal state of that can eventually be combined to form a successful network.
	www.ijcai.org /papers/1452.txt (4375 words)

	OGI School of Science & Engineering
		The challenge for neuroevolution is that difficult tasks may require complex networks with many connections, all of which must be set to the right values.
		Even if a network exists that can solve the task, evolution may not be able to find it in such a high-dimensional search space.
		By starting minimally, NEAT is more likely to find simple solutions than neuroevolution methods that begin with large fixed or randomized topologies.
	www.ogi.edu /about/events/dsp_event.cfm?event_id=D1AC5B60-0314-562A-2FE6A0860C585398 (290 words)

	Title: Numerical Optimization with Neuroevolution (Site not responding. Last check: 2007-10-19)
		Neuroevolution techniques have been successful in many sequential decision tasks such as robot control and game playing.
		This paper aims at establishing whether they can be useful in numerical optimization more generally, by comparing neuroevolution to linear programming in a manufacturing optimization domain.
		It turns out that neuroevolution can learn to compensate for uncertainty in the data and outperform linear programming when the number of variables in the problem is small and the required precision is low, but the current techniques do not (yet) provide an advantage in problems where many variables must be optimized with high precision.
	www.sal.hut.fi /Abstracts/pgre02.htm (121 words)

	nUCLEAR: The nexus for University College London Evolutionary Algorithms Research (Site not responding. Last check: 2007-10-19)
		Decision policies for such tasks are difficult to design by hand, but it is often possible to learn them through interaction with the environment.
		Neuroevolution, where neural networks are evolved with genetic algorithms, is a new and powerful method for learning such policies.
		In this talk, I will review recent advances in neuroevolution methods, and present several applications ranging from rocket control and autonomous vehicles to robotics and interactive video games.
	www.cs.ucl.ac.uk /research/nuclear/previous.html (1204 words)

	--> virtual life lab <-- utrecht university
		NEAT - NeuroEvolution with Augmented Topologies (Stanley, Miikkulainen)
		Some methods evolve topologies in addition to weights, but these usually have a bound on the complexity of networks that can be evolved and begin evolution with random topologies.
		This project is based on a neuroevolution method called NeuroEvolution of Augmenting Topologies (NEAT) that can evolve networks of unbounded complexity from a minimal starting point.
	www.aisland.org /vll/course/literature.htm (451 words)

	1. Train robots to fight with tactics of your choice 2. Play
		In the second phase, players pit their robots in a battle against those trained by some other player, to see how well their training regimens prepared their robots for battle.
		The robots in NERO use artificial neural networks for their "brains", and they learn by means of neuroevolution.
		Neuroevolution is a genetic algorithm that rewards the agents that perform the best and punishing those that perform the worst.
	www.we-make-money-not-art.com /archives/005656.php (275 words)

	A Neuroevolution Method for Dynamic Resource Allocation on a Chip Multiprocessor - Gomez, Burger, Miikkulainen ... (Site not responding. Last check: 2007-10-19)
		A Neuroevolution Method for Dynamic Resource Allocation on a Chip Multiprocessor - Gomez, Burger, Miikkulainen (ResearchIndex)
		A Neuroevolution Method for Dynamic Resource Allocation on a Chip Multiprocessor (2001)
		A neuroevolution method for dynamic resource allocation on a chip multiprocessor.
	citeseer.ist.psu.edu /gomez01neuroevolution.html (481 words)

	Genetic and Evolutionary Computation Conference - GECCO 2005 - Free Tutorials
		value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: the state can be disambiguated through recurrency, and novel situations handled through pattern matching.
		In this tutorial, we will review (1) neuroevolution methods that evolve fixed-topology networks, network topologies, and network construction processes, (2) ways of combining traditional neural network learning algorithms with evolutionary methods, and (3) applications of neuroevolution to game playing, robot control, resource optimization, and cognitive science.
		He is an author of over 150 articles on neuroevolution, connectionist natural language processing, and the computational neuroscience of the visual cortex.
	www.isgec.org /gecco-2005/free-tutorials.html (4611 words)

	Red Crocodile Productions (Site not responding. Last check: 2007-10-19)
		Intelligent agent design is a focus of both AI academics and professional game developers, but what constitutes intelligence and how best to elicit it remain hotly debated topics.
		Several populations of predators are incrementally evolved to gain skills in both pursuit and exploration which should then allow them to perform well in a real-time, one-on-one advanced Prey Capture scenario.
		This contains not only the dissertation but also the source code to the neuroevolution library developed (extended) and the experiments performed (including Prey Capture).
	www.redcrocodile.net /source/dissertation.php (279 words)

	Active Guidance for a Finless Rocket using Neuroevolution - Gomez, Miikkulainen (ResearchIndex) (Site not responding. Last check: 2007-10-19)
		Active Guidance for a Finless Rocket using Neuroevolution (2003)
		This document uses CoBlitz to cache paper downloads.
		Active guidance for a finless rocket using neuroevolution.
	citeseer.ist.psu.edu /gomez03active.html (370 words)

	ANN: EvNim uses neuroevolution to evolve an ANN that plays t (Site not responding. Last check: 2007-10-19)
		ANN: EvNim uses neuroevolution to evolve an ANN that plays t
		EvNim uses neuroevolution to evolve an ANN that plays the game of Nim.
		In this version of Nim two players begin the game with 15 "beans".
	www.groupsrv.com /science/ntopic39285.html (299 words)

	Paper: CURRICULUM VITAE :: (Site not responding. Last check: 2007-10-19)
		The challenge for neuroevolution is that dif cult tasks may require complex networks with many connections, all of which must be set to the right values.
		By starting minimally, NEAT is more likely to nd ef cient and robust solutions than neuroevolution methods that begin with large xed or randomized topologies; by elaborating on existing solutions, it can gradually construct even highly complex solutions.
		Postdoctoral Researcher, Department of Computer Sciences, The University of Texas at Austin, Since Fall 2004; Research on real-time neuroevolution in NERO and developing a neuroevolution engine for the TIELT gaming research framework.
	computing.breinestorm.net /neat+solutions+game+domains+real (566 words)

	Computer Science \| Wesley Kerr (Site not responding. Last check: 2007-10-19)
		The theoretical Knapsack problem is investigated using many different algorithms and the actual results are compared.
		When we began playing with neuroevolution we were designing a team to compete in Robocup.
		Robocup competition fell through but not before we created a program to evolve an agent to solve the task of locating the ball.
	www.cs.uwyo.edu /~wkerr/research.html (114 words)

	Python - ANN: EvNim uses neuroevolution to evolve an ANN that plays the game
		Python - ANN: EvNim uses neuroevolution to evolve an ANN that plays the game
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		ANN: EvNim uses neuroevolution to evolve an ANN that plays the game
	www.codecomments.com /archive278-2004-6-209109.html (331 words)

	Kenneth Stanley and his Virtual Robots who LEARN
		Specifically, Ken's research focuses on the area of machine learning called neuroevolution, which means programming a machine to evolve small simulated brains called neural networks through an evolutionary process.
		NERO allowed Ken to demonstrate the potential of neuroevolution in a novel way because the video game format is a convenient platform to showcase the research.
		rtNEAT (Real-time NeuroEvolution of Augmenting Topologies) is an enhancement of NEAT, a predecessor algorithm developed by Ken. rtNEAT allows the brains of robotic soldiers to evolve quickly enough to be fun for the player.
	oea.cs.utexas.edu /imagine/ken_stanley/index.html (901 words)

	NNRG - Project
		In this project, four methods were developed to harness the mechanisms of culture in neuroevolution: culling overlarge litters, mate selection by complementary competence, phenotypic diversity maintenance, and teaching offspring to respond like an elder.
		The methods are efficient because they operate without requiring additional fitness evaluations, and because each method addresses a different aspect of neuroevolution, they also combine synergetically.
		The combined system balances diversity and selection pressure, and improves performance both in terms of learning speed and solution quality in sequential decision tasks.
	nn.cs.utexas.edu /project-view.php?RECORD_KEY(Projects)=ProjID&ProjID(Projects)=38 (124 words)

	NeuroEvolution by Augmented Topologies - Term Explanation on IndexSuche.Com (Site not responding. Last check: 2007-10-19)
		NeuroEvolution by Augmented Topologies - Term Explanation on IndexSuche.Com
		Books and Others to each Topic: "NeuroEvolution by Augmented Topologies".
		A copy of the license is included in the section entitled
	www.indexsuche.com /NeuroEvolution_by_Augmented_Topologies.html (61 words)

	ai-junkie.com :: View topic - Neuroevolution smaple code to strat with
		Posted: Thu Feb 09, 2006 3:03 am Post subject: Neuroevolution smaple code to strat with
		Hi, can somebody provide me some samaple code of "Neuroevolution" or can mention me some resource.
		I have got few article to get some baisc understanding of the theory behind.
	www.ai-junkie.com /board/viewtopic.php?t=210 (139 words)

	The Spring Project :: View topic - An interesting university AI project
		NERO is the result of a joint project between the Digital Media Collaboratory (DMC) and the neuroevolution group at the Department of Computer Sciences at the University of Texas at Austin (UTCS).
		Now I've started reading stuff on this, NEAT looks promising, providing such things as effective ways of controlling resource collectors and scouts, and adaptive backup strategem for fi ever the AI's preprogrammed tactical agents arent capable of dealing with the situation.
		Lots and lots of neuroevolution papers, very interesting.
	taspring.clan-sy.com /phpbb/viewtopic.php?t=1582&start=0&postdays=0&postorder=asc&highlight= (503 words)

	Faustino Gomez - Home Page
		Dynamic Resource Allocation for a Chip-Multiprocessor using Neuroevolution.
		Robust Non-Linear Control through Neuroevolution, Department of Computer Sciences Technical Report AI-TR-03-303, August 2003.
		Robust Non-Linear Control through Neuroevolution, Department of Computer Sciences Technical Report AI-TR-02-292, October 2002.
	www.idsia.ch /~tino (503 words)

	Peter Stone: Automatic Feature Selection via Neuroevolution
		Previous efforts to automate feature selection rely on expensive meta-learning or are applicable only when labeled training data is available.
		This paper presents a novel method called FS-NEAT which extends the NEAT neuroevolution method to automatically determine an appropriate set of inputs for the networks it evolves.
		Developing a way to solve this problem automatically would make current machine learning methods much more useful.
	www.cs.utexas.edu /~pstone/Papers/bib2html/b2hd-GECCO05-fsneat.html (406 words)

	3 NeuroEvolution of Augmenting Topologies (NEAT)
		This approach is highly effective, as shown e.g.
		in comparison to other neuroevolution (NE) methods in the double pole balancing benchmark task.
		The NEAT method consists of solutions to three fundamental challenges in evolving neural network topology: (1) What kind of genetic representation would allow disparate topologies to crossover in a meaningful way?
	www.cs.cmu.edu /afs/cs/project/jair/pub/volume21/stanley04a-html/node3.html (1845 words)

	The Neuroinformatics Portal Pilot - Neuroevolution paper, software, and demo announc... (Site not responding. Last check: 2007-10-19)
		The Neuroinformatics Portal Pilot - Neuroevolution paper, software, and demo announc...
		recent years, neuroevolution, the artificial evolution of neural
		Plone makes heavy use of CSS, which means it is accessible to any internet browser, but the design needs a standards-compliant browser to look like we intended it.
	www.neuroinf.de /News/2002/11/06_07-54-57 (404 words)

	The Codex Forums :: View topic - Neuro-Evolving Robotic Operatives - NERO
		NERO is an active research project run almost entirely by students.
		It uses the real-time NeuroEvolution of Augmenting Topologies (rtNEAT) algorithm created by Ken Stanley during his PhD work at UT Austin.
		The NERO project is a collaboration of the Department of Computer Sciences and the Digital Media Collaboratory at the University of Texas at Austin.
	www.rpgcodex.com /phpBB/viewtopic.php?p=262994 (206 words)

	UTCS Artificial Intelligence Lab: Publications (Site not responding. Last check: 2007-10-19)
		Reinforcement learning approaches have made progress in such problems, but have so far not scaled well.
		Neuroevolution, has improved upon conventional reinforcement learning, but has still not been successful in full-scale, non-linear control problems.
		The method is faster than other approaches on a set of difficult learning benchmarks, and is used in two full-scale control tasks demonstrating its applicability to real world problems.
	z.cs.utexas.edu /users/ai-lab/publications_search.php?id=139 (120 words)

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